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URN

etd-0609105-162207

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Author

Kuo-Ho Su

Author's Email Address

khsu@ntist.edu.tw

Department

Electrical Engineering

Year

2004

Semester

2

Degree

Ph.D.

Type of Document

Doctoral Dissertation

Language

English

Page Count

150

Title

DESIGN AND APPLICATIONS OF HYBRID INTELLIGENT CONTROLLERS

Keyword

servo motor drive

fuzzy control

genetic algorithm

grey theorem

grey theorem

genetic algorithm

fuzzy control

servo motor drive

Abstract

Some hybrid intelligent controllers are designed for nonlinear dynamical systems in this dissertation. First, a newly-design adaptive fuzzy total sliding-mode controller (AFTSMC), in which a translation width is embedded into the fuzzy controller to reduce the chattering phenomena, is developed for perturbed electrical servo drive and tension control. In the AFTSMC, the fuzzy control rules base is compact and only one parameter needs to be adjusted. The second controller is also developed for perturbed electrical servo drive but it is designed via the approximation ability of fuzzy system to mimic the good behaviors of total sliding-mode control (TSMC) system. The third control scheme is named as supervisory enhanced genetic algorithm controller (SEGAC). It includes an enhanced genetic algorithm controller (EGAC) and a supervisory controller. In the EGAC design, the spirit of gradient descent training is embedded in GA to construct a main controller to search the optimum control effort under uncertainties. Moreover, to stabilize the system states around a defined bound region, a supervisory controller, which is derived in the sense of Lyapunov stability theorem, is added to adjust the control effort. Finally, a supervisory state feedback linearization control via grey uncertainty prediction technique is proposed to track the desired trajectory under the environment that unmodelled dynamics and external disturbances exist. The grey uncertainty predictor is designed to forecast the uncertainty and the predicted data is fed to the feedback linearization controller to evaluate the control effort on line. All the proposed control schemes are applied to electrical servo drives or other nonlinear dynamical systems by simulation and experiment to demonstrate the effectiveness and advantages.